October 7, 2025 • 41 mins
Remember the SaaS playbook that built today’s tech giants? Well, it’s officially dead. The AI revolution is rewriting the rules, leaving companies without a roadmap for what comes next.
However, Pendo CEO and co-founder, Todd Olson, is mapping out a new playbook using insights from customers, other founders, and his own team, all of whom are either beginning and in the midst of their AI transformations.
In this episode, host Trisha Price and Todd unpack how leaders can thrive when everything they know gets turned upside down. This isn’t your typical founder interview - it’s an open, honest conversation about leading when there’s no playbook to follow.
Here’s what you’ll discover:
“There’s no AI playbook. …Even the AI-native founders are just experimenting like the rest of us.” - Todd Olson
The power behind an entrepreneurial spirit. Todd shares his thoughts on what the role of a CEO needs to be during a transformation. And why his majors and minors, along with his entrepreneurial mindset, are exactly what the company needs from him right now. Todd’s lesson: Have the conviction to show that your idea can work.
How Pendo went all-in on AI. Todd reveals the strategy behind acquiring AI-native companies over established players, and how injecting new DNA changed everything from engineering practices to innovation. He also shares how his role in experimenting with AI tools inspired the rest of the company.
How to balance AI innovation with enterprise expectations. Todd explains the balancing act he and his team face with enterprise customers who still expect familiar and compliant solutions, while simultaneously introducing AI agents and workflows to guide the company through an AI transformation.
Whether you’re a founder trying to reinvent your business model or a product leader tasked with building your company’s AI strategy, this episode reveals what it actually takes to lead with entrepreneurial conviction when the only certainty is change.
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Drop us a ⭐⭐⭐⭐⭐ review, and share this episode with a teammate who’s staring down their own hard call today. Every subscription and review helps more product leaders find the show - let’s build better products together!
Presented by Pendo. Dive deeper at pendo.io or connect with Trisha Price on LinkedIn.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
If you build software or lead peoplewho do, then you're in the right place.
This is Hard Calls, real decisions,real leaders, real outcomes.
Hi everyone.
Welcome back to Hard Calls, thepodcast where we bring the best product
leaders from all around the worldto talk about best practices, tips,
(00:20):
tricks, scaling lessons, and of coursethe Hard Calls they've had to make.
Today we're mixing it up a little bitand we are live from Pendo's headquarters
in Raleigh, North Carolina, and I amwith the co-founder and CEO Todd Olson.
Hi, Todd.
Hey, Trisha.
How you doing?
Good.
Good, good.
Well, as you know and the listeners know,we always start Hard Calls with talking
(00:44):
about a hard call you've had to make.
But before we jump into that, I wanteveryone to get to know you a little bit
and how we work together a little bit,and then we'll get into the hard call.
Sounds good.
Sounds good.
So.
For all of you guys, I was hiredas the CPO here at Pendo about
four years ago when Todd hired mein, and I often get the question.
(01:05):
How is it to be a CPO and work for Todd?
Most of you probably already know Todd isone of the top product thought leaders,
has been a product leader for most of hiscareer before coming and founding Pendo.
He wrote the book on being product-ledand so obviously being a CPO and
(01:25):
working for someone like Todd canprobably feel intimidating to many
people and they often just ask me like,
How did you find your footing?
How was it to work with him?
So I'd love for you to just share likewhat your thoughts are on bringing in
a CPO and how that, how that works.
Well, first off, I think veryfew things intimidate you.
So I, I think, you need to findsomeone who comes from, is cut from
(01:49):
that cloth that, I mean, obviously ifI'm speaking to someone and they're
intimidated by me, it's just gonnabe a bad relationship from the start.
I mean, I don't think any relationshipin life is positive where like
one person is intimidated or hasissues with the other person,
especially not a leadership position.
But look, I, I think you want, and Ithink you did this well, is you wanna find
(02:11):
someone who wants to partner with you.
Yeah.
Who doesn't try to like box you out,shuts you out, rather finds a way,
in a very healthy way to sort ofmake room for you within the org.
I mean, reality is running an orgat scale - there's a lot of things
I probably don't wanna do, likea lot of things I don't wanna do.
A lot of things I didn't do.
(02:31):
Career frameworks and ladders
But even like the detailsof execution around a lot of
like, which team specifically?
Like no one wants mein that sausage making.
Yeah.
And heck, I don't, I doubt you wereeven in that sausage making, you
probably had one or two levels beneathyou doing it, and you're providing, of
course, accountability and oversightand leadership to all those folks.
So like, look, I, I think what youneed is someone who finds space and,
(02:55):
and wants to collaborate and wantsto work and like we, we had a handful
of sessions that, I remember vividlywhere you brought me in and we had
conversations like sometimes, folks, oneof our little hacks that we did that I
thought was a lot of fun, and we stilldo to this day is like sort of evening
meetings, dinner meetings at the office.
(03:17):
Yeah.
We, we do some takeout, usually sushi.
Yeah.
We're in a conference room.
We got whiteboard markers goingand we're just like talking.
Yeah.
Brainstorming.
Yeah.
with, with, everyone'sidea is, is good and equal.
I think that worked.
I remember that one sessionwe had on our embedded guides,
our embedded content feature.
(03:39):
Yeah.
And I remember just sitting there andlike getting your feedback and getting
your opinion and putting it up there.
And it's not like you directed me,you can't do this or you have to
do this, but I want your feedback.
You know the product, you're the founder,you know the space better than anyone.
So for us to be able to collaborate onit but not feel like it was directive,
I think was critical for both of us.
Well, I hope it was fun for me.
(04:00):
Me too.
And I hope it was fun for everyone,I think it wasn't just you.
You had, you had, you, you had I thinka level beneath you and a level, but
Yeah.
So yeah, they were all in the room.
Yeah.
And I think, I think at the end ofthe day, I'm a builder and I like to
build things and it brings me joy.
And, and I think, I like to thinkI, I my happiest self in some
(04:21):
role where I can still build.
And I'm not just stuck in, everyoneassumes I'm just, the CEO I'm
stuck in a bunch of meetings with.
I don't know, bankers and financepeople or whatever, sometimes.
And, and I have to do those things,that's part of my job as well.
But I, I think that the buildingpart, that's where the magic happens.
Yeah.
In companies and that like, so yeah,I think you need a relationship
where people don't feel threatened.
(04:43):
They feel like each person understandstheir space, their role, but, and
look, and then it's having directconversations like, Hey, this didn't
work for me, or, Hey, I'd like this more.
I think.
Having a good enough relationshipwhere you can be open and honest.
Yeah.
And direct with each other
Yeah for sure.
'Cause you're not gonna be,
It's not gonna be perfect every time.
No.
You're gonna like weall move very quickly.
(05:07):
Yeah.
When you move quickly, sometimesyou forget to do something.
You unintentionally exclude someone.
May not even be me, maybe someone else.
And like, I think working throughthat, that's really, really important.
Yeah.
Well, I appreciate it and um.
It's just something people ask meall the time and it's hard to explain
how to make relationships work andworking relationships and trust, but
(05:29):
I think it's something we did reallywell and hope that sharing that's
helpful for, for everybody else.
Well the key is sushi at the office.
Sushi at the office
after hours.
You know that, that.
It fixes most relationships.
Maybe, maybe a bottle ofwine with a sushi, maybe.
Yeah, a little wine won't,won't hurt the creativity.
It doesn't.
It doesn't.
It doesn't.
So, Todd, this is Hard Calls, so I'd lovefor you to share with us one of the Hard
(05:52):
Calls you've had to make in your careerthat was, fairly career defining for you.
Yeah.
Look I think, there are so many.
I'm gonna go back to one pre-Pendo 'causeI think it was one of the like, the sort
of like interesting hard calls I made.
I was, so this is at Rally Softwarejust prior to Pendo, this is maybe two
(06:17):
to three years before I started Pendo.
I was, I saw this opportunity tobuild this add-on product and, I
was not the head of product there.
I had no power, no control,no resources to do it.
I was in product marketing and but Ihad this opportunity and I saw this
product and actually some customerbuilt some open source product using
(06:43):
our APIs and it was like, wow, allof our customers could use this.
I mean, I'm talkingabout product market fit.
When you have a customer build an add-onto your product that uses your data,
you're like, okay, I know I want that.
So I convinced a bunch of people to hiresome consultants to take this open source
product and start productizing it withcustomers, over the next three to six
(07:06):
months that, that, outsource team grew andgrew and grew into it was like honestly
kind of a full fledge engineering team,to the point where finally the CFO kind
of woke up and was like, what's going on?
We have like, is this like some slushfund that you're like directing funds to?
And, that ended up becoming, aportfolio management add-on to our agile
(07:29):
project management solution, which,the company ultimately sold to Computer
Associates, which was in that space.
But it was, I think it was that notletting title, not letting role.
I eventually took over productshortly thereafter, so then I
actually had probably helped abilityto move the whole, like move a bunch
of engineering resources onto it.
We, we pulled it in-house, but I thinkjust not letting things get in your way.
(07:53):
I think that's how you make hard calls andlike having conviction around something.
So, so yeah, I'd say that's it.
I love that.
I have often given productmanagers the advice when they ask
me like, how do you get promoted?
I'm like, well, if you work ata high growth company, you don't
have to wait till there's a jobopening or your boss leaves.
It's, I've always said, gocreate a hill and go stand on it.
(08:14):
And that's exactly what you did.
You created a hill.
Yeah.
And so many people, they get hung upon their title or their role or this
and that, and like, man, just go do it.
Build a business case.
Yeah.
And just go, go get after it.
Make it happen.
And, and I, I think, and look thatI wasn't the CEO of the company.
I, I think the, the reason I likethat story is that plenty of people
(08:36):
can say, well, now you can make hardcalls because, blah, blah, blah.
But I think anyone can makea hard call in the company.
Yeah.
So you just have to have some conviction.
And what you did and pulled off ishard, but I do think today with the
tools at our disposal, especiallywith AI, doing what you did back then
should be even easier for people.
(08:58):
Yes.
Yeah.
Well, yeah.
Yeah.
Well, now I, I could've like,had AI take this open source
project and work on it a bunch.
Right.
You wouldn't need all that.
Yeah.
No, I, I think, well nowit's a different world.
And yeah.
And then speaking of course of hardcalls, I, I think we are in a world
today, we're literally, every techcompany is being faced with hard call,
after hard call, after hard call.
(09:19):
And, it's completelyunique in dynamic times.
And, and while I used an older example,pre Pendo, I could have very easily
chosen a number of of examples thatare like far more recent, like in the
last few weeks or the last few months.
Yeah, because I think now is a time whereI am, being not forced is the wrong word,
'cause it means that it feelslike something, something's
(09:40):
pushing me to do it.
I, but I feel conviction aroundthe need to make harder calls.
Yeah.
And is a lot of that just because ofmarket dynamics, is that because of AI in
your belief in how software's changing?
Well, yeah.
I, I think.
You wanna be capturing and jumping ontosome of the most interesting waves.
(10:03):
If you think about what sort of pulledPendo along, you could argue sort of
the SaaS wave when people were, yeah,building up software as a service
and the whole cloud 100 communityand like, like we sold to pretty
much every one of those customers.
And that, and the growth ofthose businesses fuel our growth.
And that's kind of why we're sittinghere in this nice office space.
(10:24):
We grabbed onto that wave, butthe next wave or current wave
you could even argue is AI.
And we need to find a way tolasso that and jump on it 'cause
that is gonna fuel the next fivemaybe even 10 years of our growth.
So, so the way I think of it is,is you want to be on the waves.
It's gonna like the natural marketforces to drive the next level of growth.
(10:47):
But then it's also very, very clearthat these tools are redefining
the way you run businesses.
Mm-hmm.
And, and there's, it's, it, it's, it'schanging the way we develop software.
It's changing the way we market to people.
Yeah.
It's changing the way we'regonna be selling to people.
And because it's all very, verynew, like there, the winners and
(11:12):
leaders are gonna be ones thatkind of figure out how to do it.
Because the truth is there isno, like, people talk about,
There's no playbook right now for this.
Yeah.
People talk about the SaaS,playbook's dead, you gotta do AI
stuff, but they don't say that.
There's no AI playbook!
Yeah.
And I, I speak with founders,both founders of AI companies
who are younger than me.
You could say they're AI native.
Yep.
Their age actually doesn't really matter.
(11:32):
It it, what matters is they've beencreating AI companies from start.
And I, I talk with 'em tolearn like, what are you doing?
And I get some good nuggetsand there's some obvi.
We're all experimenting, butthe truth is they're just
experimenting like the rest of us.
Yeah.
And they have yet to figure it out.
And because the truthis we just don't know.
We just don't know what, what,how to do things the right way.
(11:54):
How to use these solutions.
And the tools are changing
Yeah.
Nearly every week.
Yeah.
And so that makes it very exciting.
A little bit scary, but as someone who'sa, honestly a lifelong entrepreneur.
Yeah.
I've been starting company since I was 20.
To me, I feel like the companyneeds me to be more entrepreneurial.
(12:17):
Go back to sort of this day onemindset that Amazon talks about.
I think that is what the companyneeds for me now, because the reality
is that at nearly 900 employees,we have a lot of folks here slash
leaders here who we hired becausethey're good at scaling big things.
Mm-hmm.
Which is not the same thing as goingback to AI native and no rethinking how
(12:40):
you, it's very different skill sets.
Yeah.
And, and you're at thisinteresting inflection.
You actually need both.
Correct.
You need scale, you have enterprisecustomers, you have a pretty
big company at this point.
Yet at the same time, you've gotta kindof start over with how you think about
your product, supporting customers, howyou do business, like, how do you do
both of those things at the same time?
Yeah.
We can't be like, you know,cowboys running this company.
(13:04):
We're a decent sized business.
Yeah.
And, and like we need professionalHR practices and finance practices.
We're audited by a bigfive accounting firm.
We need these things obviously.
Yet you kind of have to like becomfortable throwing certain things away.
Yeah.
And experimenting.
And the question is, who at thecompany has experience such interest
(13:29):
in going back to sort of day one.
And, and the truth is, it's likeI'm one of the few people who's
very comfortable in that world.
Yeah.
So the company needs that out ofme more and, and while I still do
plenty of scale things, I mean, I'm,I've got like 12 hours of QBRs this
week, or no tiny company would do.
And maybe we won't do in five years,years, maybe not, I don't know.
(13:50):
I honestly don't know.
But I'm gonna do 'em the next two weeks.
I don't, not because I think it's,I think it's a good practice.
I like reflection, but yes,but yeah, I think it's, yeah,
super interesting and dynamic.
It is really interesting and I lovewhat you're doing and challenging all
of the status quo and processes andtools and the way we've done things.
(14:11):
I think in product, when I talk toproduct managers, product leaders,
I mean, the first thing I tell themis if you are not trying out all
of these AI tools, if you're notprototyping, if you're not changing
up your product development lifecycle.
And I'm not even talking aboutputting AI in your product yet.
I'm just talking about using it.
(14:32):
I tell people all the time as a productmanager, you are going to be completely
lost and not have a job in a very shortperiod of time because I think it is the
most interesting time to be in product.
But if you're not jumping onthis wave and trying these new
tools, you're just gonna be lost.
No, a thousand percent.
And, and I include myself in thatworld and that that was one of the
(14:52):
things that it took me longer thanI would've liked, but for a while,
obviously as is happening, I goto our leadership team, you at the
time, others, we need to do more.
We need to do more.
Let's do this, let's do that.
And we started doing things.
I think we did our first AI hackathonlike pretty early years ago.
Our AI launch was pretty early.
Years ago.
(15:12):
Yeah.
Yeah.
And that was sort of our, you, wecan call it 1.0, we can call it 0.5.
I don't know.
I don't like naming things, but that wasour first foray and just like touching,
experimenting, and you could see someteams we had were just more natural early
adopters of it, playing around with it.
Right.
And, we did, we did well, wegot out there in the market.
(15:34):
We started experimenting with customers,but I, I just felt very anxious the
whole time we weren't doing enough.
Now, I couldn't put my fingeron what we needed to do.
Right.
And I couldn't say, put three teams onA, B, C 'cause I had no idea at a what,
a what A, B, C really was at the time.
But I knew in my heart, I just have,I felt anxious for the last few years.
Yeah.
Which is a weird feeling to have.
(15:55):
And it's not constructive either,just me walking around being anxious.
But I think what happened is aboutnine-ish months ago, maybe longer, I
really started personally playing around,like you just said, product managers
should be playing around and using this.
I started playing aroundwith it a lot more.
Yeah, I started experimenting, notjust with Chat GPT, which everyone sort
of experimented with, but I, I startedexperimenting with those, some of the
(16:17):
prototyping tools, the prototypingtools and the code generations for
zero and Lovable and things like that.
And, and as someone who is a programmerprofessionally, and I don't get paid
for it now, but I used to get paidfor it, so, I was a bit skeptical
at first of all this, but once Istarted using it, I was blown away.
It is
And yeah.
Is it right all the time?
(16:38):
No.
And does it like generatecode that has bugs?
Yes.
Yeah.
But if you tell it, go fix it,it tries and does a decent job.
And if you give it more directionand, and then the other thing is the
models are getting better and better.
So like maybe one week itgenerates sort of the wrong thing
and then three weeks later itgenerates sort of the right thing.
Yeah.
I mean, you can kind of see whereit's going and you're like, wow.
(17:01):
Wow.
And I still felt it was valuable forme to have technical skills, but it was
also valuable that I have product skills.
And I have, I mean, I'm not a designerthat, that, that's the interesting thing
is a lot of us who are builders, we have
majors and minors and look Icannot design things, period.
(17:26):
So I'm just gonna say period.
No one's ever put me in front of Figma.
I've never used Photoshop in my life.
I think I have a taste.
I have things I like.
You have an eye for it.
You have an eye for it.
I appreciate certain thingsthat are well designed.
And my wife doesn't think so aboutfurniture, but I think I have a decent
taste, but certainly on software design,I think I have pretty good taste.
(17:48):
But then of course, product, Iwould say is sort of a major of
mine I've been doing now for years.
I feel like I have a prettygood skill set there.
And then of course, engineeringand coding, I have a good skill
set, but I probably am rusty, notprobably, let's just say I am rusty.
Me too.
AI sort of fills in those gaps.
(18:08):
It does.
And that, that's the power that I startedseeing is like, it takes someone like
me who is a major in something andbut I have some weaknesses in other
areas and it fills in those weaknesses.
Yeah.
And I get a lot done.
Yeah.
Now maybe it's not like productionquality for like a Pendo, but
it's a pretty darn good prototypethat I could hand off to someone.
(18:29):
And, and once, once I started playingaround with it more and touching it
more it really helped drive a lot moreclarity about what we needed to do.
And then others in the org started playingaround with it more and that's when you
realize just what's leadership about?
It's about setting an example.
Mm-hmm.
And if I'm not playing aroundwith it, which candidly
I can't expect everyone else to
(18:49):
Yeah.
That, that really hit home.
And look, I think the Shopifymemo, which was earlier this year.
Yeah.
And some people didn't like it.
Some people did like it.
I had a chance to hear, the presidentof Shopify, Harley, speak at a
small event and they use this termreflexive and it resonates with me.
(19:09):
'Cause I don't know, I don't know aboutyou, but like, I still reflexively
use Google for a lot of things.
Mm-hmm.
I have a question?
I Google it.
I've kind of switched over to ChatGPT.
My kids make fun of me all the time.
They're like, mom, that's somethingyou could actually just Google,
but I have kind of switched myreflex now is to go to ChatGBT.
Well, I'm still working on it.
(19:30):
I mean, I would say I'm 50/50 now.
Yeah.
But, but I'm not 80/20.
And, and maybe there is a worldwhere like, depending on your, your,
your kids', point, like Google'sgonna be better at certain things.
Certain things.
That's what their point.
They're like, if youdon't need an opinion.
Just go to Google.
(19:50):
Yeah.
You know?
Well, and when you go to Google now,it gives you an AI summary at the
top, which is pretty darn close.
It's pretty good.
It's, which is pretty good.
So like, like we're seeing thesesort of like different ways of
working evolve and we're allexperimenting their own personal hacks.
But if we don't do it ourselves,like no one else, we can't
expect our org to do it.
So, you've talked about leadingthe way for your AI revolution at
(20:15):
the company, by playing with tools.
You also went out and did a couple ofexciting acquisitions in the AI space.
Yeah.
And so how do you think about that?
Like how do you think about, obviouslywhen you're talking about internal and
ways to work, it's about tinkering andexperimenting and playing, but when
you're talking about changing yourproduct, for all of us product folks,
(20:38):
acquisition is a great way to do it.
Yeah.
Look, I, I think it was clear thatwe needed to inject some different
experiences, different skill sets,different DNA, Yeah, in the business.
When we, so the first acquisition we madein the AI space was Zelta AI, and that
was the result of actually us being, Iwould say, intentional in our MA strategy.
(21:00):
Yeah.
We went out looking for a productthat sort of solved that problem and
the problem was very, very specificand everyone in product knows it.
We have lots of qualitative data.
It's sort of like scattered across ourorg in these different systems, yes
a PM could go and read through every
(21:21):
Call log and support tickets,
Yeah.
Zendesk ticket and like, pull out the,
Enhancement requests
And no one does that.
But it's super valuable.
Yeah.
And there's like gold in those hills.
Yeah.
I like to say.
And if we can find a way to ingest it alland, and, and really surface those things
(21:43):
that's incredibly valuable to customers.
And so we kind of set out to sort of,be very intentional about acquiring
it as, as and, and then it was superinteresting 'cause as you meet companies
and there, some of our investorsguided us on this, that you actually
almost wanted younger companies.
Yeah.
Which may be counterintuitive because ifyou started too long ago, you were pre
(22:07):
LLM and a lot of those companies wererewriting their stacks to leverage LLMs.
Right.
We wanted specifically the skillsets and the tech that was post.
Exactly.
LLMs
Because they're just buildingthings in different ways
And that's the skillset we needed.
Exactly.
We didn't need peoplewho were learning it.
The same way we were learningand trying to pivot to it.
We needed people who natively thoughtand built their products that way.
(22:29):
Exactly.
Yeah.
And, and so that's what we did.
And, and I think it's been successfulon a number of fronts: One the
product's great, we're excited about it.
It's in market.
I've gotten a lot of positivefeedback from customers around it.
Some CEO grabbed me the other weekat a conference, told me how I was
transforming their product practices.
So that felt really, really good.
But also like the DNA change, like,whether it's like the engineering
(22:54):
team operates differently.
A little more agile, they'vebeen using AI tools from day one.
Yeah.
One of the things I, I was seeing anengineering meeting with one of the
engineers from, from Zelta in a biggerengineering room and it was all about
how to use AI in engineering and abunch of engineers were talking about.
(23:17):
Well, it couldn't handle this tailrecursion example that I had or whatever
they, I think a number of folks wereexperimenting with things and like you
passed some sophisticated algorithm andit got confused, et cetera, et cetera.
And the engineer from Zelta,everyone's breaking it basically
(23:39):
and can't use it for this.
Can't use it for this,can't use it for this.
And one thing that Mick saidthat really struck with me, he is
like, look, I can spend two hourstyping or 30 minutes reviewing.
And it's just faster to spend30 minutes reviewing code than
it is two hours a type code.
And it changed my mental model tolike, are a lot of us gonna start
(24:02):
really honing our editorial skills?
Yeah.
Where we may not be generatinglarge swaths of content,
but we may be curating it.
Yeah.
We may be editing it.
Yeah.
And like, if you think about the worldwe're in now, like we love editors and
curators that like, those are the peoplethat are influencers across social media.
(24:22):
Like the people that we all follow areessentially people pulling out the good
bits and surfacing it to us 'cause that'swhat they think we should be looking at.
And I think, so we've already, as aculture, started moving in this direction.
Yeah, I think we're gonna.
Yeah, large languagemodels and generative AI
It is gonna push us all to be aset of curators, editors and that's
(24:45):
actually pretty exciting for me.
It is.
So, it is, it's really exciting.
And then you followed on from Zeltaand did another AI acquisition as well,
Correct!
Yeah.
Yeah.
And we've done two acquisitionsin 12, well, three actually,
technically we acquired a smallcommunity called Product Collective.
But yes, this is the mostaggressive we've been in market,
(25:05):
since we started the company.
We've done, we averaged by oneevery two years prior to this.
And now we're, we've doneseveral in, in one year.
And that was Forwrd.ai andthat was another intentional
purchase.
We know that our data is super valuablefor certain business outcomes, like
(25:27):
detecting churn or expansion signals,which ultimately they on driving
revenue, like scoring leads or productqualified leads, things like that.
But it's a hard problem.
Yeah, it's a hard problem.
It not only do you have our rawdata, which sort of needs to be
cleansed and sort of massaged and youneed to sort of, you know, join it
(25:48):
with CRM data and other data, likeit's a relatively hard challenge.
And look, some teams, some companieshave data science teams and they, four
or five people, maybe half dozen peoplethat can sort of do all that work.
I mean, we do, we do.
We have been doing that on.
But that's a rarer skillset.
Yeah.
And that when I talk to our customers,even some of our larger enterprise
(26:09):
customers, they aren't doing that.
Yeah.
And this is an opportunity to, to takeour data and honestly drive revenue for
our customers because when I talk to ourcustomers, a lot of 'em kind of, they
wanna do this, they're trying to do this;they have limited skill sets, limited,
like the skill you need is like takingPendo data and sort of making it relevant.
(26:33):
Right.
It's, and they just don'tunderstand it to the degree.
And the Pendo data is.
Is nuanced to it's nuanced, get theright insights out, sophisticated.
It does need to be, it doesdepend on like how you set up your
subscription in some instances.
Yep.
And we have all theexpertise in that on that.
But what we've seen is this, this,our data is a really, really good
non-biased, early warning signal for
(26:55):
That's really cool.
For all these opportunities.
So yeah, that, that's beenreally, really exciting.
And, um.
And it's just a great team.
Another thing comes down to, I,I've talked to hopefully in both
instances, yes, the product, we wereintentional, the use case we liked,
but the team's adding something else.
Yeah.
And this entrepreneurialmindset, AI talent, different
points of view of doing things.
(27:16):
Yeah.
I mean, Kobe, was a product leader inour space so, obviously bringing his
background and experience to Pendo is,is super helpful, super, super helpful.
Well, we already had a team in Israel,so like that in some ways that made
it actually really, really easy.
Yeah.
Yeah.
In an office, likeliterally it, it closed.
Next day everyone's in an office doinga town hall, having lunch together.
(27:38):
That's great.
So, um.
Yeah,
Culture's important with these things.
So both of the acquisitions that youtalked about, were really using AI
to glean insights into your product.
Correct.
Into our product.
Mm-hmm.
But I know a lot of listeners, us too,when we think about AI, we think about
(28:00):
insights, but we also think aboutthis whole agentic world and where
automation of workflows is going.
Conversational interfaces is goingand that's something I know that,
that you've been working on, not justfor Pendo and our product, but also
supporting everybody else, all the otherproduct people out there as well, and
building their agentic experiences.
(28:22):
So how, how are you, how are youinstilling those new skills and talents?
Because that's a different skill setthan gleaning the insights that you were
talking about from Zelta and Forwrd.
Correct, correct.
Well, the first thing before I moveon from insights, I will say like the
first thing we sort of prioritizedand built, which, I feel really good
(28:43):
about was it was clear as you'restarting to, Hey, what do we do?
At some level, we, we are measuringuser interfaces and experiences.
There's no question that the world'smoving where we're gonna have, we're
still gonna have clicks and scrolls andpage loads but some percentage of our
user interfaces are gonna be agentic,chat-like in nature, whatever, whatever,
(29:07):
copilot-ish - and there's just so manydifferent terms - and I don't know if that
percentage is gonna be, like, some peopleprobably are theorizing, it's gonna be
a hundred percent of user experiences.
I'm probably not on that boat whereit's gonna be a hundred percent.
I think power users are gonnawant different experiences.
I know I, as a power user forsoftware, like I just wanna,
(29:29):
Somebody just wanna go do something.
I know I can do it.
I just wanna go in andclick a button or two.
Like Right.
I, yeah.
I don't need to like, type in asentence every time I wanna do anything.
Right.
So I personally don't think it'll bea hundred percent, but I think a lot
of people are gonna try to do it andthey're gonna experiment with it.
So, but regardless of what it is.
I know that people are gonna measurethat part of their app, and if we see
(29:49):
all these other stuff and we don'tsee this, then we're not providing a
hundred percent visibility into howpeople are experiencing your product.
So, so the first thing we, we sort ofprioritize what we're calling agent
analytics, which leveraging the same Pendoinstall, the same Pendo infrastructure.
You can basically direct usto say, Hey, take this agent.
It starts pulling in the actualconversation, and start giving
(30:10):
you insights into how people aredoing it when they're doing it.
How they're working in concert withthe other parts of your product,
start getting like honestly just 360degree visibility of what's going on.
So that was kinda step one.
And that's powerful because asstep two for us is okay, now we
(30:30):
need to add sort of that userinterface element to our product.
Yeah.
And that different way of experiencingthe product and when we first started
going down this path, honestly, I, wemade an intentional decision to sort
of let teams run their own experimentsindependently, we did not centralize it.
(30:51):
Yeah.
We didn't create onepiece of infrastructure.
There's just so many different typesof infrastructure and so many different
decisions, we decided that pickingone too early could lead us down
a path where, pick the wrong one.
Yeah.
So like, let teams experiment,give people like creative license.
And we did that and, we ended up with,three agents, four agents, whatever,
(31:15):
you, whatever, I don't know whatthe exact number is, but a number of
different agentic experiences in Pendo.
Yeah.
Now you're gonna startseeing us sort of like
Bring that together
Now that we we know enough toknow what's working and what's not
working, start creating some commoninfrastructure, common UI elements.
We have an opinion now, almost apoint of view like, so to taste, so
(31:38):
to speak, and what we think the rightexperience is long term and that's the
process we're sort of going through now.
So as product leaders, this isa conversation I have with other
product leaders all the time.
Which is not the technical challenge ofdoing what you're about to do, but the
emotional and change management of it.
Because you go and you let your variousteams and product teams build what
(32:04):
they wanna build, and they all thinktheirs is the coolest and the best.
And there probably are pieces of each oneof them that are the coolest and the best.
But then you come up with sort of a, acombined point of view of where you wanna
go with one experience, and then everyonehas to kill what they had built before.
And there's like this real senseof loss 'cause the best product
(32:27):
managers love what they built.
They fall in love with it.
Yep.
And then you gotta tell'em their baby's ugly.
Or even if their baby's notugly, it's not the prettiest.
And so how are you dealing with that?
In terms of, helping people stillfeel connected to the strategy
and, and not feel that loss.
Yeah.
Look, I think we treat all of thesethings as, this goes back to the, the
(32:50):
very classic book of Lean Startup, butI, I see, I always say like, the value
in, in, in all of this work, whether youthrow it or not, is validated learnings.
Yeah.
We have valid, we have a validatedlearning around, a, a certain path
or a certain technology set maybeit doesn't scale the way we want.
Maybe it doesn't quitehit our quality bar.
Maybe it's hard to maintain.
I, I think we're gonna have to takesome risks and be comfortable throwing
(33:17):
things away in this new environment.
Yeah.
Just like we did day one.
Yeah.
We're gonna make mistakes and, andtechnology's we're gonna wake up one day
and we're like, oh wow, 05 just droppedand it's like freaking amazing, you know?
And like, we need to moveeverything to that, you know?
Yeah.
Sorry, GPT5.
(33:39):
Yeah I think it's just tryingto get, reprogram people's
mindset to be like, don't get tooattached or married to anything.
Yeah.
And, but you also need toiterate very, very quickly.
Yeah.
Like if you threw away 18 monthsof work, that's different than
throwing away six weeks of work.
Right.
So, so we're trying to move very, veryquickly and continually evaluate it.
Yeah.
(34:00):
I think it's important for all productmanagers and product leaders always to
think this way (34:08):
quick experimentation.
Don't get too tied up insomething or your idea, you know?
But right now with AI and how quicklythings are moving and changing, and
what technology is allowing us todo today that it didn't two weeks
ago, not two years ago, two weeksago, more than ever, I think we all
have to be comfortable with that.
(34:29):
Yeah.
Well look, one of my classicexpressions in product is don't
get married to your roadmap.
Yep.
Another one of my classicexpressions is it's not your roadmap.
It's my roadmap,meaningit's the company's roadmap.
Yeah, that's right.
Just like it's not your budget,it's the company's budget.
That's right.
Like you are curating it, yourmanaging it on behalf of the company.
But if you think it's yours,you are candidly wrong.
(34:52):
No, we, we owe outcomes to our investors.
Yeah.
And we owe value to our customers.
Yeah, that's it.
It's, that's what the roadmap is,
You knows, and if it'swrong, we will change it.
Yeah.
And I am completely unafraid.
And, look, yeah.
You have to deal with the, theconstant, oh, everything's changing
and we can't even like, stay in onething long enough to like, you know.
(35:12):
Okay, great.
Yeah.
The world is changing right now.
The world's changing.
I'm not changing it, you know?
Yeah.
But we're gonna, like,
We didn't invent GPT five, butwe better take advantage of it.
Exactly.
Like, if, if you're not comfortablesort of like adapting to the
environment, you will miss out onthings and I don't miss out on anything.
Yeah.
Like, like that's certainlynot how we're gonna roll here.
(35:33):
Yeah.
Agree.
So Todd, we've talked a lot today about AIand the impact to Pendo, both using AI to
build our products and sell our productsand everything, and then also agentic
experiences and insights in the product.
But we also have a lot of sophisticatedenterprise customers who love AI and
(36:00):
are partnering with us on this AIjourney, but they're still what I
would call regular features and thingsthat they need and expect from us.
So how do you balance those things?
How do you make sure you're taking careof your customers in the traditional
sense, not just in terms of leading thecharge with innovation and acquisitions?
(36:20):
Yeah.
I mean, look, I think.
The title of the podcast is Hard Calls.
This is a hard call.
Yeah.
Like what, what do you spend on, variouspieces, in various, parts of the roadmap.
I, I think this is, this is why weall get paid the big bucks in product,
is to make those, those decisions.
And look, the, the truth is you're gonnahave to invest in a lot of things for
(36:41):
your enterprise customers, especially acompany like Pendo, which is still sort
of on its journey towards the enterprise.
Yeah.
Features like bulk operations.
And look, I, I know that, someengineers or other, well, that's
not the exciting features.
It's exciting for large enterprises.
It's, yeah, if it's, if you justsave them like 20 hours of work
(37:03):
because you did something like inone click versus like them spending
an hour or 20 hour, whatever it is.
Like, like those are thefeatures you need to do.
I mean, but, but we often forget isthat large enterprises, some of 'em
are heavily software so that we,we protect them as they're sort of
like going through their journeys.
Now the cool thing is, is these two sortof pillars are coming together where,
(37:27):
these large enterprises are, many of'em are trying to experiment with AI.
They want to experiment with AI.
They may not be exactly ready for it thismoment, but they're gonna want a partner
like us who is already meeting theirneeds in other areas of their business
to sort of help them along that journey.
Yeah, and I think that's where we thinkthe real opportunity is, is while we're
(37:50):
now experimenting with it with maybe ourmore innovative customers are, are smaller
customers, startups even are starting to,to, leverage some of our AI solutions.
We'll work out the kinks, we'll perfectthem, we'll get them, in a really, uh,
production quality sort of like positionand then we'll be able to bring a lot of
(38:15):
those to, to our enterprise customers.
And I think it's gonna bea win-win for everyone.
But, but yeah, you have tobalance all these things.
And it's gonna be interestingis that as enterprises adopt AI,
they're gonna want more controls.
Yeah.
They want,
Specific permissions.
We can use this, but we can't use this.
We can use certain types of AI butwe can't use other types of AI.
(38:39):
Exactly like they're gonna wantpotentially to use their own large
language models that are possiblytuned for their own environment,
Self hosted.
And yeah,
Yeah there's a lot of examples likethat that are things that we would do
for our large enterprise customers.
Yeah.
Like, there's really good reasonsfor those companies to require
that, which still allow us todeliver a great product experience.
So why?
(38:59):
Like, I don't necessarily,I'm not super concerned.
Now, that's not ever like a version1.0 feature you're gonna build,
but yeah, I think you have to payattention to both needs and you have
to invest heavily in both and yeah,we, we have a lot of customers.
If I showed up at our Pendomonium.
which is our user conference andsaid, we're only building AI features.
(39:22):
People get upset.
Yeah.
They get upset and rightly so.
Yeah.
I don't think it's the right decision.
Yeah.
But we're gonna invest a lotin AI because I think it will,
the enterprises will want it.
Yeah.
We're already seeing, like some ofour large enterprise customers ask us
for things, I mean, agent analyticsis a great example where we have some
large enterprise customers that arestarting to experiment with it and
(39:43):
use it and because they have mandatesto play around with AI and they just
wanna know, are these things working?
Yeah.
What is it,
What are people doing with it?
What are they trying to solve?
How are they using it?
Who's using it?
Exactly.
And, and, and because when I talk to someof large enterprises, they're not seeing
necessarily the the the benefits yet?
(40:05):
Yeah.
That they're reading about in the news,so they're hoping by better understanding
it, understanding how people areusing it, they can tweak and tune and
ultimately get those, those outcomes.
So, so yeah, I think it's a constantbalancing act and we'll see.
We'll see how it goes.
Great.
Well, Todd, thank you forbeing on Hard Calls today.
This is obviously a fun episodefor me to record, and just really
(40:28):
appreciate you sharing your experiencewith us in terms of Pendo and really
fun to be here, live in Raleigh.
Well, it's fun for me too, so thank you.
Thank you.
Thank you for listening to HardCalls, the Product podcast, where
we share best practices and allthe things you need to succeed.
If you enjoyed the show today, sharewith your friends and come back for more.
If you build software or lead peoplewho do, then you're in the right place.
This is Hard Calls, real decisions,real leaders, real outcomes.
Hi everyone.
Welcome back to Hard Calls, thepodcast where we bring the best product
leaders from all around the worldto talk about best practices, tips,
(00:20):
tricks, scaling lessons, and of coursethe Hard Calls they've had to make.
Today we're mixing it up a little bitand we are live from Pendo's headquarters
in Raleigh, North Carolina, and I amwith the co-founder and CEO Todd Olson.
Hi, Todd.
Hey, Trisha.
How you doing?
Good.
Good, good.
Well, as you know and the listeners know,we always start Hard Calls with talking
(00:44):
about a hard call you've had to make.
But before we jump into that, I wanteveryone to get to know you a little bit
and how we work together a little bit,and then we'll get into the hard call.
Sounds good.
Sounds good.
So.
For all of you guys, I was hiredas the CPO here at Pendo about
four years ago when Todd hired mein, and I often get the question.
(01:05):
How is it to be a CPO and work for Todd?
Most of you probably already know Todd isone of the top product thought leaders,
has been a product leader for most of hiscareer before coming and founding Pendo.
He wrote the book on being product-ledand so obviously being a CPO and
(01:25):
working for someone like Todd canprobably feel intimidating to many
people and they often just ask me like,
How did you find your footing?
How was it to work with him?
So I'd love for you to just share likewhat your thoughts are on bringing in
a CPO and how that, how that works.
Well, first off, I think veryfew things intimidate you.
So I, I think, you need to findsomeone who comes from, is cut from
(01:49):
that cloth that, I mean, obviously ifI'm speaking to someone and they're
intimidated by me, it's just gonnabe a bad relationship from the start.
I mean, I don't think any relationshipin life is positive where like
one person is intimidated or hasissues with the other person,
especially not a leadership position.
But look, I, I think you want, and Ithink you did this well, is you wanna find
(02:11):
someone who wants to partner with you.
Yeah.
Who doesn't try to like box you out,shuts you out, rather finds a way,
in a very healthy way to sort ofmake room for you within the org.
I mean, reality is running an orgat scale - there's a lot of things
I probably don't wanna do, likea lot of things I don't wanna do.
A lot of things I didn't do.
(02:31):
Career frameworks and ladders
But even like the detailsof execution around a lot of
like, which team specifically?
Like no one wants mein that sausage making.
Yeah.
And heck, I don't, I doubt you wereeven in that sausage making, you
probably had one or two levels beneathyou doing it, and you're providing, of
course, accountability and oversightand leadership to all those folks.
So like, look, I, I think what youneed is someone who finds space and,
(02:55):
and wants to collaborate and wantsto work and like we, we had a handful
of sessions that, I remember vividlywhere you brought me in and we had
conversations like sometimes, folks, oneof our little hacks that we did that I
thought was a lot of fun, and we stilldo to this day is like sort of evening
meetings, dinner meetings at the office.
(03:17):
Yeah.
We, we do some takeout, usually sushi.
Yeah.
We're in a conference room.
We got whiteboard markers goingand we're just like talking.
Yeah.
Brainstorming.
Yeah.
with, with, everyone'sidea is, is good and equal.
I think that worked.
I remember that one sessionwe had on our embedded guides,
our embedded content feature.
(03:39):
Yeah.
And I remember just sitting there andlike getting your feedback and getting
your opinion and putting it up there.
And it's not like you directed me,you can't do this or you have to
do this, but I want your feedback.
You know the product, you're the founder,you know the space better than anyone.
So for us to be able to collaborate onit but not feel like it was directive,
I think was critical for both of us.
Well, I hope it was fun for me.
(04:00):
Me too.
And I hope it was fun for everyone,I think it wasn't just you.
You had, you had, you, you had I thinka level beneath you and a level, but
Yeah.
So yeah, they were all in the room.
Yeah.
And I think, I think at the end ofthe day, I'm a builder and I like to
build things and it brings me joy.
And, and I think, I like to thinkI, I my happiest self in some
(04:21):
role where I can still build.
And I'm not just stuck in, everyoneassumes I'm just, the CEO I'm
stuck in a bunch of meetings with.
I don't know, bankers and financepeople or whatever, sometimes.
And, and I have to do those things,that's part of my job as well.
But I, I think that the buildingpart, that's where the magic happens.
Yeah.
In companies and that like, so yeah,I think you need a relationship
where people don't feel threatened.
(04:43):
They feel like each person understandstheir space, their role, but, and
look, and then it's having directconversations like, Hey, this didn't
work for me, or, Hey, I'd like this more.
I think.
Having a good enough relationshipwhere you can be open and honest.
Yeah.
And direct with each other
Yeah for sure.
'Cause you're not gonna be,
It's not gonna be perfect every time.
No.
You're gonna like weall move very quickly.
(05:07):
Yeah.
When you move quickly, sometimesyou forget to do something.
You unintentionally exclude someone.
May not even be me, maybe someone else.
And like, I think working throughthat, that's really, really important.
Yeah.
Well, I appreciate it and um.
It's just something people ask meall the time and it's hard to explain
how to make relationships work andworking relationships and trust, but
(05:29):
I think it's something we did reallywell and hope that sharing that's
helpful for, for everybody else.
Well the key is sushi at the office.
Sushi at the office
after hours.
You know that, that.
It fixes most relationships.
Maybe, maybe a bottle ofwine with a sushi, maybe.
Yeah, a little wine won't,won't hurt the creativity.
It doesn't.
It doesn't.
It doesn't.
So, Todd, this is Hard Calls, so I'd lovefor you to share with us one of the Hard
(05:52):
Calls you've had to make in your careerthat was, fairly career defining for you.
Yeah.
Look I think, there are so many.
I'm gonna go back to one pre-Pendo 'causeI think it was one of the like, the sort
of like interesting hard calls I made.
I was, so this is at Rally Softwarejust prior to Pendo, this is maybe two
(06:17):
to three years before I started Pendo.
I was, I saw this opportunity tobuild this add-on product and, I
was not the head of product there.
I had no power, no control,no resources to do it.
I was in product marketing and but Ihad this opportunity and I saw this
product and actually some customerbuilt some open source product using
(06:43):
our APIs and it was like, wow, allof our customers could use this.
I mean, I'm talkingabout product market fit.
When you have a customer build an add-onto your product that uses your data,
you're like, okay, I know I want that.
So I convinced a bunch of people to hiresome consultants to take this open source
product and start productizing it withcustomers, over the next three to six
(07:06):
months that, that, outsource team grew andgrew and grew into it was like honestly
kind of a full fledge engineering team,to the point where finally the CFO kind
of woke up and was like, what's going on?
We have like, is this like some slushfund that you're like directing funds to?
And, that ended up becoming, aportfolio management add-on to our agile
(07:29):
project management solution, which,the company ultimately sold to Computer
Associates, which was in that space.
But it was, I think it was that notletting title, not letting role.
I eventually took over productshortly thereafter, so then I
actually had probably helped abilityto move the whole, like move a bunch
of engineering resources onto it.
We, we pulled it in-house, but I thinkjust not letting things get in your way.
(07:53):
I think that's how you make hard calls andlike having conviction around something.
So, so yeah, I'd say that's it.
I love that.
I have often given productmanagers the advice when they ask
me like, how do you get promoted?
I'm like, well, if you work ata high growth company, you don't
have to wait till there's a jobopening or your boss leaves.
It's, I've always said, gocreate a hill and go stand on it.
(08:14):
And that's exactly what you did.
You created a hill.
Yeah.
And so many people, they get hung upon their title or their role or this
and that, and like, man, just go do it.
Build a business case.
Yeah.
And just go, go get after it.
Make it happen.
And, and I, I think, and look thatI wasn't the CEO of the company.
I, I think the, the reason I likethat story is that plenty of people
(08:36):
can say, well, now you can make hardcalls because, blah, blah, blah.
But I think anyone can makea hard call in the company.
Yeah.
So you just have to have some conviction.
And what you did and pulled off ishard, but I do think today with the
tools at our disposal, especiallywith AI, doing what you did back then
should be even easier for people.
(08:58):
Yes.
Yeah.
Well, yeah.
Yeah.
Well, now I, I could've like,had AI take this open source
project and work on it a bunch.
Right.
You wouldn't need all that.
Yeah.
No, I, I think, well nowit's a different world.
And yeah.
And then speaking of course of hardcalls, I, I think we are in a world
today, we're literally, every techcompany is being faced with hard call,
after hard call, after hard call.
(09:19):
And, it's completelyunique in dynamic times.
And, and while I used an older example,pre Pendo, I could have very easily
chosen a number of of examples thatare like far more recent, like in the
last few weeks or the last few months.
Yeah, because I think now is a time whereI am, being not forced is the wrong word,
'cause it means that it feelslike something, something's
(09:40):
pushing me to do it.
I, but I feel conviction aroundthe need to make harder calls.
Yeah.
And is a lot of that just because ofmarket dynamics, is that because of AI in
your belief in how software's changing?
Well, yeah.
I, I think.
You wanna be capturing and jumping ontosome of the most interesting waves.
(10:03):
If you think about what sort of pulledPendo along, you could argue sort of
the SaaS wave when people were, yeah,building up software as a service
and the whole cloud 100 communityand like, like we sold to pretty
much every one of those customers.
And that, and the growth ofthose businesses fuel our growth.
And that's kind of why we're sittinghere in this nice office space.
(10:24):
We grabbed onto that wave, butthe next wave or current wave
you could even argue is AI.
And we need to find a way tolasso that and jump on it 'cause
that is gonna fuel the next fivemaybe even 10 years of our growth.
So, so the way I think of it is,is you want to be on the waves.
It's gonna like the natural marketforces to drive the next level of growth.
(10:47):
But then it's also very, very clearthat these tools are redefining
the way you run businesses.
Mm-hmm.
And, and there's, it's, it, it's, it'schanging the way we develop software.
It's changing the way we market to people.
Yeah.
It's changing the way we'regonna be selling to people.
And because it's all very, verynew, like there, the winners and
(11:12):
leaders are gonna be ones thatkind of figure out how to do it.
Because the truth is there isno, like, people talk about,
There's no playbook right now for this.
Yeah.
People talk about the SaaS,playbook's dead, you gotta do AI
stuff, but they don't say that.
There's no AI playbook!
Yeah.
And I, I speak with founders,both founders of AI companies
who are younger than me.
You could say they're AI native.
Yep.
Their age actually doesn't really matter.
(11:32):
It it, what matters is they've beencreating AI companies from start.
And I, I talk with 'em tolearn like, what are you doing?
And I get some good nuggetsand there's some obvi.
We're all experimenting, butthe truth is they're just
experimenting like the rest of us.
Yeah.
And they have yet to figure it out.
And because the truthis we just don't know.
We just don't know what, what,how to do things the right way.
(11:54):
How to use these solutions.
And the tools are changing
Yeah.
Nearly every week.
Yeah.
And so that makes it very exciting.
A little bit scary, but as someone who'sa, honestly a lifelong entrepreneur.
Yeah.
I've been starting company since I was 20.
To me, I feel like the companyneeds me to be more entrepreneurial.
(12:17):
Go back to sort of this day onemindset that Amazon talks about.
I think that is what the companyneeds for me now, because the reality
is that at nearly 900 employees,we have a lot of folks here slash
leaders here who we hired becausethey're good at scaling big things.
Mm-hmm.
Which is not the same thing as goingback to AI native and no rethinking how
(12:40):
you, it's very different skill sets.
Yeah.
And, and you're at thisinteresting inflection.
You actually need both.
Correct.
You need scale, you have enterprisecustomers, you have a pretty
big company at this point.
Yet at the same time, you've gotta kindof start over with how you think about
your product, supporting customers, howyou do business, like, how do you do
both of those things at the same time?
Yeah.
We can't be like, you know,cowboys running this company.
(13:04):
We're a decent sized business.
Yeah.
And, and like we need professionalHR practices and finance practices.
We're audited by a bigfive accounting firm.
We need these things obviously.
Yet you kind of have to like becomfortable throwing certain things away.
Yeah.
And experimenting.
And the question is, who at thecompany has experience such interest
(13:29):
in going back to sort of day one.
And, and the truth is, it's likeI'm one of the few people who's
very comfortable in that world.
Yeah.
So the company needs that out ofme more and, and while I still do
plenty of scale things, I mean, I'm,I've got like 12 hours of QBRs this
week, or no tiny company would do.
And maybe we won't do in five years,years, maybe not, I don't know.
(13:50):
I honestly don't know.
But I'm gonna do 'em the next two weeks.
I don't, not because I think it's,I think it's a good practice.
I like reflection, but yes,but yeah, I think it's, yeah,
super interesting and dynamic.
It is really interesting and I lovewhat you're doing and challenging all
of the status quo and processes andtools and the way we've done things.
(14:11):
I think in product, when I talk toproduct managers, product leaders,
I mean, the first thing I tell themis if you are not trying out all
of these AI tools, if you're notprototyping, if you're not changing
up your product development lifecycle.
And I'm not even talking aboutputting AI in your product yet.
I'm just talking about using it.
(14:32):
I tell people all the time as a productmanager, you are going to be completely
lost and not have a job in a very shortperiod of time because I think it is the
most interesting time to be in product.
But if you're not jumping onthis wave and trying these new
tools, you're just gonna be lost.
No, a thousand percent.
And, and I include myself in thatworld and that that was one of the
(14:52):
things that it took me longer thanI would've liked, but for a while,
obviously as is happening, I goto our leadership team, you at the
time, others, we need to do more.
We need to do more.
Let's do this, let's do that.
And we started doing things.
I think we did our first AI hackathonlike pretty early years ago.
Our AI launch was pretty early.
Years ago.
(15:12):
Yeah.
Yeah.
And that was sort of our, you, wecan call it 1.0, we can call it 0.5.
I don't know.
I don't like naming things, but that wasour first foray and just like touching,
experimenting, and you could see someteams we had were just more natural early
adopters of it, playing around with it.
Right.
And, we did, we did well, wegot out there in the market.
(15:34):
We started experimenting with customers,but I, I just felt very anxious the
whole time we weren't doing enough.
Now, I couldn't put my fingeron what we needed to do.
Right.
And I couldn't say, put three teams onA, B, C 'cause I had no idea at a what,
a what A, B, C really was at the time.
But I knew in my heart, I just have,I felt anxious for the last few years.
Yeah.
Which is a weird feeling to have.
(15:55):
And it's not constructive either,just me walking around being anxious.
But I think what happened is aboutnine-ish months ago, maybe longer, I
really started personally playing around,like you just said, product managers
should be playing around and using this.
I started playing aroundwith it a lot more.
Yeah, I started experimenting, notjust with Chat GPT, which everyone sort
of experimented with, but I, I startedexperimenting with those, some of the
(16:17):
prototyping tools, the prototypingtools and the code generations for
zero and Lovable and things like that.
And, and as someone who is a programmerprofessionally, and I don't get paid
for it now, but I used to get paidfor it, so, I was a bit skeptical
at first of all this, but once Istarted using it, I was blown away.
It is
And yeah.
Is it right all the time?
(16:38):
No.
And does it like generatecode that has bugs?
Yes.
Yeah.
But if you tell it, go fix it,it tries and does a decent job.
And if you give it more directionand, and then the other thing is the
models are getting better and better.
So like maybe one week itgenerates sort of the wrong thing
and then three weeks later itgenerates sort of the right thing.
Yeah.
I mean, you can kind of see whereit's going and you're like, wow.
(17:01):
Wow.
And I still felt it was valuable forme to have technical skills, but it was
also valuable that I have product skills.
And I have, I mean, I'm not a designerthat, that, that's the interesting thing
is a lot of us who are builders, we have
majors and minors and look Icannot design things, period.
(17:26):
So I'm just gonna say period.
No one's ever put me in front of Figma.
I've never used Photoshop in my life.
I think I have a taste.
I have things I like.
You have an eye for it.
You have an eye for it.
I appreciate certain thingsthat are well designed.
And my wife doesn't think so aboutfurniture, but I think I have a decent
taste, but certainly on software design,I think I have pretty good taste.
(17:48):
But then of course, product, Iwould say is sort of a major of
mine I've been doing now for years.
I feel like I have a prettygood skill set there.
And then of course, engineeringand coding, I have a good skill
set, but I probably am rusty, notprobably, let's just say I am rusty.
Me too.
AI sort of fills in those gaps.
(18:08):
It does.
And that, that's the power that I startedseeing is like, it takes someone like
me who is a major in something andbut I have some weaknesses in other
areas and it fills in those weaknesses.
Yeah.
And I get a lot done.
Yeah.
Now maybe it's not like productionquality for like a Pendo, but
it's a pretty darn good prototypethat I could hand off to someone.
(18:29):
And, and once, once I started playingaround with it more and touching it
more it really helped drive a lot moreclarity about what we needed to do.
And then others in the org started playingaround with it more and that's when you
realize just what's leadership about?
It's about setting an example.
Mm-hmm.
And if I'm not playing aroundwith it, which candidly
I can't expect everyone else to
(18:49):
Yeah.
That, that really hit home.
And look, I think the Shopifymemo, which was earlier this year.
Yeah.
And some people didn't like it.
Some people did like it.
I had a chance to hear, the presidentof Shopify, Harley, speak at a
small event and they use this termreflexive and it resonates with me.
(19:09):
'Cause I don't know, I don't know aboutyou, but like, I still reflexively
use Google for a lot of things.
Mm-hmm.
I have a question?
I Google it.
I've kind of switched over to ChatGPT.
My kids make fun of me all the time.
They're like, mom, that's somethingyou could actually just Google,
but I have kind of switched myreflex now is to go to ChatGBT.
Well, I'm still working on it.
(19:30):
I mean, I would say I'm 50/50 now.
Yeah.
But, but I'm not 80/20.
And, and maybe there is a worldwhere like, depending on your, your,
your kids', point, like Google'sgonna be better at certain things.
Certain things.
That's what their point.
They're like, if youdon't need an opinion.
Just go to Google.
(19:50):
Yeah.
You know?
Well, and when you go to Google now,it gives you an AI summary at the
top, which is pretty darn close.
It's pretty good.
It's, which is pretty good.
So like, like we're seeing thesesort of like different ways of
working evolve and we're allexperimenting their own personal hacks.
But if we don't do it ourselves,like no one else, we can't
expect our org to do it.
So, you've talked about leadingthe way for your AI revolution at
(20:15):
the company, by playing with tools.
You also went out and did a couple ofexciting acquisitions in the AI space.
Yeah.
And so how do you think about that?
Like how do you think about, obviouslywhen you're talking about internal and
ways to work, it's about tinkering andexperimenting and playing, but when
you're talking about changing yourproduct, for all of us product folks,
(20:38):
acquisition is a great way to do it.
Yeah.
Look, I, I think it was clear thatwe needed to inject some different
experiences, different skill sets,different DNA, Yeah, in the business.
When we, so the first acquisition we madein the AI space was Zelta AI, and that
was the result of actually us being, Iwould say, intentional in our MA strategy.
(21:00):
Yeah.
We went out looking for a productthat sort of solved that problem and
the problem was very, very specificand everyone in product knows it.
We have lots of qualitative data.
It's sort of like scattered across ourorg in these different systems, yes
a PM could go and read through every
(21:21):
Call log and support tickets,
Yeah.
Zendesk ticket and like, pull out the,
Enhancement requests
And no one does that.
But it's super valuable.
Yeah.
And there's like gold in those hills.
Yeah.
I like to say.
And if we can find a way to ingest it alland, and, and really surface those things
(21:43):
that's incredibly valuable to customers.
And so we kind of set out to sort of,be very intentional about acquiring
it as, as and, and then it was superinteresting 'cause as you meet companies
and there, some of our investorsguided us on this, that you actually
almost wanted younger companies.
Yeah.
Which may be counterintuitive because ifyou started too long ago, you were pre
(22:07):
LLM and a lot of those companies wererewriting their stacks to leverage LLMs.
Right.
We wanted specifically the skillsets and the tech that was post.
Exactly.
LLMs
Because they're just buildingthings in different ways
And that's the skillset we needed.
Exactly.
We didn't need peoplewho were learning it.
The same way we were learningand trying to pivot to it.
We needed people who natively thoughtand built their products that way.
(22:29):
Exactly.
Yeah.
And, and so that's what we did.
And, and I think it's been successfulon a number of fronts: One the
product's great, we're excited about it.
It's in market.
I've gotten a lot of positivefeedback from customers around it.
Some CEO grabbed me the other weekat a conference, told me how I was
transforming their product practices.
So that felt really, really good.
But also like the DNA change, like,whether it's like the engineering
(22:54):
team operates differently.
A little more agile, they'vebeen using AI tools from day one.
Yeah.
One of the things I, I was seeing anengineering meeting with one of the
engineers from, from Zelta in a biggerengineering room and it was all about
how to use AI in engineering and abunch of engineers were talking about.
(23:17):
Well, it couldn't handle this tailrecursion example that I had or whatever
they, I think a number of folks wereexperimenting with things and like you
passed some sophisticated algorithm andit got confused, et cetera, et cetera.
And the engineer from Zelta,everyone's breaking it basically
(23:39):
and can't use it for this.
Can't use it for this,can't use it for this.
And one thing that Mick saidthat really struck with me, he is
like, look, I can spend two hourstyping or 30 minutes reviewing.
And it's just faster to spend30 minutes reviewing code than
it is two hours a type code.
And it changed my mental model tolike, are a lot of us gonna start
(24:02):
really honing our editorial skills?
Yeah.
Where we may not be generatinglarge swaths of content,
but we may be curating it.
Yeah.
We may be editing it.
Yeah.
And like, if you think about the worldwe're in now, like we love editors and
curators that like, those are the peoplethat are influencers across social media.
(24:22):
Like the people that we all follow areessentially people pulling out the good
bits and surfacing it to us 'cause that'swhat they think we should be looking at.
And I think, so we've already, as aculture, started moving in this direction.
Yeah, I think we're gonna.
Yeah, large languagemodels and generative AI
It is gonna push us all to be aset of curators, editors and that's
(24:45):
actually pretty exciting for me.
It is.
So, it is, it's really exciting.
And then you followed on from Zeltaand did another AI acquisition as well,
Correct!
Yeah.
Yeah.
And we've done two acquisitionsin 12, well, three actually,
technically we acquired a smallcommunity called Product Collective.
But yes, this is the mostaggressive we've been in market,
(25:05):
since we started the company.
We've done, we averaged by oneevery two years prior to this.
And now we're, we've doneseveral in, in one year.
And that was Forwrd.ai andthat was another intentional
purchase.
We know that our data is super valuablefor certain business outcomes, like
(25:27):
detecting churn or expansion signals,which ultimately they on driving
revenue, like scoring leads or productqualified leads, things like that.
But it's a hard problem.
Yeah, it's a hard problem.
It not only do you have our rawdata, which sort of needs to be
cleansed and sort of massaged and youneed to sort of, you know, join it
(25:48):
with CRM data and other data, likeit's a relatively hard challenge.
And look, some teams, some companieshave data science teams and they, four
or five people, maybe half dozen peoplethat can sort of do all that work.
I mean, we do, we do.
We have been doing that on.
But that's a rarer skillset.
Yeah.
And that when I talk to our customers,even some of our larger enterprise
(26:09):
customers, they aren't doing that.
Yeah.
And this is an opportunity to, to takeour data and honestly drive revenue for
our customers because when I talk to ourcustomers, a lot of 'em kind of, they
wanna do this, they're trying to do this;they have limited skill sets, limited,
like the skill you need is like takingPendo data and sort of making it relevant.
(26:33):
Right.
It's, and they just don'tunderstand it to the degree.
And the Pendo data is.
Is nuanced to it's nuanced, get theright insights out, sophisticated.
It does need to be, it doesdepend on like how you set up your
subscription in some instances.
Yep.
And we have all theexpertise in that on that.
But what we've seen is this, this,our data is a really, really good
non-biased, early warning signal for
(26:55):
That's really cool.
For all these opportunities.
So yeah, that, that's beenreally, really exciting.
And, um.
And it's just a great team.
Another thing comes down to, I,I've talked to hopefully in both
instances, yes, the product, we wereintentional, the use case we liked,
but the team's adding something else.
Yeah.
And this entrepreneurialmindset, AI talent, different
points of view of doing things.
(27:16):
Yeah.
I mean, Kobe, was a product leader inour space so, obviously bringing his
background and experience to Pendo is,is super helpful, super, super helpful.
Well, we already had a team in Israel,so like that in some ways that made
it actually really, really easy.
Yeah.
Yeah.
In an office, likeliterally it, it closed.
Next day everyone's in an office doinga town hall, having lunch together.
(27:38):
That's great.
So, um.
Yeah,
Culture's important with these things.
So both of the acquisitions that youtalked about, were really using AI
to glean insights into your product.
Correct.
Into our product.
Mm-hmm.
But I know a lot of listeners, us too,when we think about AI, we think about
(28:00):
insights, but we also think aboutthis whole agentic world and where
automation of workflows is going.
Conversational interfaces is goingand that's something I know that,
that you've been working on, not justfor Pendo and our product, but also
supporting everybody else, all the otherproduct people out there as well, and
building their agentic experiences.
(28:22):
So how, how are you, how are youinstilling those new skills and talents?
Because that's a different skill setthan gleaning the insights that you were
talking about from Zelta and Forwrd.
Correct, correct.
Well, the first thing before I moveon from insights, I will say like the
first thing we sort of prioritizedand built, which, I feel really good
(28:43):
about was it was clear as you'restarting to, Hey, what do we do?
At some level, we, we are measuringuser interfaces and experiences.
There's no question that the world'smoving where we're gonna have, we're
still gonna have clicks and scrolls andpage loads but some percentage of our
user interfaces are gonna be agentic,chat-like in nature, whatever, whatever,
(29:07):
copilot-ish - and there's just so manydifferent terms - and I don't know if that
percentage is gonna be, like, some peopleprobably are theorizing, it's gonna be
a hundred percent of user experiences.
I'm probably not on that boat whereit's gonna be a hundred percent.
I think power users are gonnawant different experiences.
I know I, as a power user forsoftware, like I just wanna,
(29:29):
Somebody just wanna go do something.
I know I can do it.
I just wanna go in andclick a button or two.
Like Right.
I, yeah.
I don't need to like, type in asentence every time I wanna do anything.
Right.
So I personally don't think it'll bea hundred percent, but I think a lot
of people are gonna try to do it andthey're gonna experiment with it.
So, but regardless of what it is.
I know that people are gonna measurethat part of their app, and if we see
(29:49):
all these other stuff and we don'tsee this, then we're not providing a
hundred percent visibility into howpeople are experiencing your product.
So, so the first thing we, we sort ofprioritize what we're calling agent
analytics, which leveraging the same Pendoinstall, the same Pendo infrastructure.
You can basically direct usto say, Hey, take this agent.
It starts pulling in the actualconversation, and start giving
(30:10):
you insights into how people aredoing it when they're doing it.
How they're working in concert withthe other parts of your product,
start getting like honestly just 360degree visibility of what's going on.
So that was kinda step one.
And that's powerful because asstep two for us is okay, now we
(30:30):
need to add sort of that userinterface element to our product.
Yeah.
And that different way of experiencingthe product and when we first started
going down this path, honestly, I, wemade an intentional decision to sort
of let teams run their own experimentsindependently, we did not centralize it.
(30:51):
Yeah.
We didn't create onepiece of infrastructure.
There's just so many different typesof infrastructure and so many different
decisions, we decided that pickingone too early could lead us down
a path where, pick the wrong one.
Yeah.
So like, let teams experiment,give people like creative license.
And we did that and, we ended up with,three agents, four agents, whatever,
(31:15):
you, whatever, I don't know whatthe exact number is, but a number of
different agentic experiences in Pendo.
Yeah.
Now you're gonna startseeing us sort of like
Bring that together
Now that we we know enough toknow what's working and what's not
working, start creating some commoninfrastructure, common UI elements.
We have an opinion now, almost apoint of view like, so to taste, so
(31:38):
to speak, and what we think the rightexperience is long term and that's the
process we're sort of going through now.
So as product leaders, this isa conversation I have with other
product leaders all the time.
Which is not the technical challenge ofdoing what you're about to do, but the
emotional and change management of it.
Because you go and you let your variousteams and product teams build what
(32:04):
they wanna build, and they all thinktheirs is the coolest and the best.
And there probably are pieces of each oneof them that are the coolest and the best.
But then you come up with sort of a, acombined point of view of where you wanna
go with one experience, and then everyonehas to kill what they had built before.
And there's like this real senseof loss 'cause the best product
(32:27):
managers love what they built.
They fall in love with it.
Yep.
And then you gotta tell'em their baby's ugly.
Or even if their baby's notugly, it's not the prettiest.
And so how are you dealing with that?
In terms of, helping people stillfeel connected to the strategy
and, and not feel that loss.
Yeah.
Look, I think we treat all of thesethings as, this goes back to the, the
(32:50):
very classic book of Lean Startup, butI, I see, I always say like, the value
in, in, in all of this work, whether youthrow it or not, is validated learnings.
Yeah.
We have valid, we have a validatedlearning around, a, a certain path
or a certain technology set maybeit doesn't scale the way we want.
Maybe it doesn't quitehit our quality bar.
Maybe it's hard to maintain.
I, I think we're gonna have to takesome risks and be comfortable throwing
(33:17):
things away in this new environment.
Yeah.
Just like we did day one.
Yeah.
We're gonna make mistakes and, andtechnology's we're gonna wake up one day
and we're like, oh wow, 05 just droppedand it's like freaking amazing, you know?
And like, we need to moveeverything to that, you know?
Yeah.
Sorry, GPT5.
(33:39):
Yeah I think it's just tryingto get, reprogram people's
mindset to be like, don't get tooattached or married to anything.
Yeah.
And, but you also need toiterate very, very quickly.
Yeah.
Like if you threw away 18 monthsof work, that's different than
throwing away six weeks of work.
Right.
So, so we're trying to move very, veryquickly and continually evaluate it.
Yeah.
(34:00):
I think it's important for all productmanagers and product leaders always to
think this way (34:08):
quick experimentation.
Don't get too tied up insomething or your idea, you know?
But right now with AI and how quicklythings are moving and changing, and
what technology is allowing us todo today that it didn't two weeks
ago, not two years ago, two weeksago, more than ever, I think we all
have to be comfortable with that.
(34:29):
Yeah.
Well look, one of my classicexpressions in product is don't
get married to your roadmap.
Yep.
Another one of my classicexpressions is it's not your roadmap.
It's my roadmap,meaningit's the company's roadmap.
Yeah, that's right.
Just like it's not your budget,it's the company's budget.
That's right.
Like you are curating it, yourmanaging it on behalf of the company.
But if you think it's yours,you are candidly wrong.
(34:52):
No, we, we owe outcomes to our investors.
Yeah.
And we owe value to our customers.
Yeah, that's it.
It's, that's what the roadmap is,
You knows, and if it'swrong, we will change it.
Yeah.
And I am completely unafraid.
And, look, yeah.
You have to deal with the, theconstant, oh, everything's changing
and we can't even like, stay in onething long enough to like, you know.
(35:12):
Okay, great.
Yeah.
The world is changing right now.
The world's changing.
I'm not changing it, you know?
Yeah.
But we're gonna, like,
We didn't invent GPT five, butwe better take advantage of it.
Exactly.
Like, if, if you're not comfortablesort of like adapting to the
environment, you will miss out onthings and I don't miss out on anything.
Yeah.
Like, like that's certainlynot how we're gonna roll here.
(35:33):
Yeah.
Agree.
So Todd, we've talked a lot today about AIand the impact to Pendo, both using AI to
build our products and sell our productsand everything, and then also agentic
experiences and insights in the product.
But we also have a lot of sophisticatedenterprise customers who love AI and
(36:00):
are partnering with us on this AIjourney, but they're still what I
would call regular features and thingsthat they need and expect from us.
So how do you balance those things?
How do you make sure you're taking careof your customers in the traditional
sense, not just in terms of leading thecharge with innovation and acquisitions?
(36:20):
Yeah.
I mean, look, I think.
The title of the podcast is Hard Calls.
This is a hard call.
Yeah.
Like what, what do you spend on, variouspieces, in various, parts of the roadmap.
I, I think this is, this is why weall get paid the big bucks in product,
is to make those, those decisions.
And look, the, the truth is you're gonnahave to invest in a lot of things for
(36:41):
your enterprise customers, especially acompany like Pendo, which is still sort
of on its journey towards the enterprise.
Yeah.
Features like bulk operations.
And look, I, I know that, someengineers or other, well, that's
not the exciting features.
It's exciting for large enterprises.
It's, yeah, if it's, if you justsave them like 20 hours of work
(37:03):
because you did something like inone click versus like them spending
an hour or 20 hour, whatever it is.
Like, like those are thefeatures you need to do.
I mean, but, but we often forget isthat large enterprises, some of 'em
are heavily software so that we,we protect them as they're sort of
like going through their journeys.
Now the cool thing is, is these two sortof pillars are coming together where,
(37:27):
these large enterprises are, many of'em are trying to experiment with AI.
They want to experiment with AI.
They may not be exactly ready for it thismoment, but they're gonna want a partner
like us who is already meeting theirneeds in other areas of their business
to sort of help them along that journey.
Yeah, and I think that's where we thinkthe real opportunity is, is while we're
(37:50):
now experimenting with it with maybe ourmore innovative customers are, are smaller
customers, startups even are starting to,to, leverage some of our AI solutions.
We'll work out the kinks, we'll perfectthem, we'll get them, in a really, uh,
production quality sort of like positionand then we'll be able to bring a lot of
(38:15):
those to, to our enterprise customers.
And I think it's gonna bea win-win for everyone.
But, but yeah, you have tobalance all these things.
And it's gonna be interestingis that as enterprises adopt AI,
they're gonna want more controls.
Yeah.
They want,
Specific permissions.
We can use this, but we can't use this.
We can use certain types of AI butwe can't use other types of AI.
(38:39):
Exactly like they're gonna wantpotentially to use their own large
language models that are possiblytuned for their own environment,
Self hosted.
And yeah,
Yeah there's a lot of examples likethat that are things that we would do
for our large enterprise customers.
Yeah.
Like, there's really good reasonsfor those companies to require
that, which still allow us todeliver a great product experience.
So why?
(38:59):
Like, I don't necessarily,I'm not super concerned.
Now, that's not ever like a version1.0 feature you're gonna build,
but yeah, I think you have to payattention to both needs and you have
to invest heavily in both and yeah,we, we have a lot of customers.
If I showed up at our Pendomonium.
which is our user conference andsaid, we're only building AI features.
(39:22):
People get upset.
Yeah.
They get upset and rightly so.
Yeah.
I don't think it's the right decision.
Yeah.
But we're gonna invest a lotin AI because I think it will,
the enterprises will want it.
Yeah.
We're already seeing, like some ofour large enterprise customers ask us
for things, I mean, agent analyticsis a great example where we have some
large enterprise customers that arestarting to experiment with it and
(39:43):
use it and because they have mandatesto play around with AI and they just
wanna know, are these things working?
Yeah.
What is it,
What are people doing with it?
What are they trying to solve?
How are they using it?
Who's using it?
Exactly.
And, and, and because when I talk to someof large enterprises, they're not seeing
necessarily the the the benefits yet?
(40:05):
Yeah.
That they're reading about in the news,so they're hoping by better understanding
it, understanding how people areusing it, they can tweak and tune and
ultimately get those, those outcomes.
So, so yeah, I think it's a constantbalancing act and we'll see.
We'll see how it goes.
Great.
Well, Todd, thank you forbeing on Hard Calls today.
This is obviously a fun episodefor me to record, and just really
(40:28):
appreciate you sharing your experiencewith us in terms of Pendo and really
fun to be here, live in Raleigh.
Well, it's fun for me too, so thank you.
Thank you.
Thank you for listening to HardCalls, the Product podcast, where
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